Chapter 2
###### Chapter 2 Phytoplanktpn #########
## PHYTOPLANKTON COMMUNITY DIVERSITY ETC -----------------------------------
#'*year-monthly biomass (1) ---------------*
# Plot monthly total biomass, comparing years
biomass.YM <- Sp_only %>%
group_by(months, month, year, Season) %>%
summarise(monthlyMean = mean(log10(meanCells), na.rm = TRUE),
monthlyN = n(),
monthlySD = sd(log10(meanCells), na.rm = TRUE),
monthlySE = sd(log10(meanCells), na.rm = TRUE)/sqrt(n())) %>%
ggplot(., aes(x = reorder(months, month), y = (monthlyMean), col = year, group = year)) +
geom_point(size = 1) +
#geom_line(size = 0.8) +
geom_errorbar(aes(ymin = monthlyMean - monthlySE, ymax = monthlyMean + monthlySE), width=0.2) +
xlab("Months") +
ylab("Log10 Cells/L") +
labs(col = "Year") +
ggtitle("Comparing the log10 monthly mean phytoplankton counts between three consecutive years, with their standard error bars") +
theme_classic() +
theme(plot.title = element_text(hjust = 0.5))
biomass.YM
# STATS for biomass differences between years and months
sp_mat <- Sp_only %>%
group_by(month, year, Species) %>%
summarise(avg = mean(meanCells, na.rm = TRUE)) %>%
pivot_wider(names_from = "Species", values_from = "avg",
values_fill = list(avg = 0))
sp_mat <- Sp_only %>%
ungroup() %>%
select(Date, months, year, Species, meanCells) %>%
pivot_wider(names_from = "Species", values_from = "meanCells",
values_fill = list(meanCells = 0))
#
# com <- sp_mat[, 4:ncol(sp_mat)]
# com <- as.matrix(com)
# com <- vegdist(com, distance = "bray")
# sp_grou <- sp_mat[, 2:3]
#ano <- anosim(com, grouping = sp_grou$year, distance = "bray")
#ano
#adon <- adonis2(com ~ months, data = sp_grou, method = "bray", permutations = 999)
###############
#'*year-monthly metrics (1)*
## comparing years over months for indices
#'*(1) ------------------*
# Richness
richness.YM <- dailyDesc %>%
group_by(months, month, year, Season) %>%
summarise(monthlyRichness = mean(S, na.rm = TRUE),
monthlyRichnessN = n(),
monthlyRichnessSD = sd(S, na.rm = TRUE),
monthlyRichnessSE = sd(S, na.rm = TRUE)/sqrt(n())) %>%
ggplot(., aes(x = reorder(months, month), y = (monthlyRichness), col = year, group = year)) +
geom_point(size = 1) +
#geom_line(size = 0.8) +
geom_errorbar(aes(ymin = monthlyRichness - monthlyRichnessSE, ymax = monthlyRichness + monthlyRichnessSE), width=0.2) +
xlab("Months") +
ylab("Richness") +
# ggtitle("Comparing the monthly species richness of phytoplankton counts between three consecutive years ") +
theme_classic() +
theme(plot.title = element_text(hjust = 0.5))
#'*(1) -----------------*
# Evenness (J')
evenness.YM <- dailyDesc %>%
group_by(months, month, year, Season) %>%
summarise(monthlyEvenness = mean(J, na.rm = TRUE),
monthlyEvennessN = n(),
monthlyEvennessD = sd(J, na.rm = TRUE),
monthlyEvennessSE = sd(J, na.rm = TRUE)/sqrt(n())) %>%
ggplot(., aes(x = reorder(months, month), y = (monthlyEvenness), col = year, group = year)) +
geom_point(size = 1) +
#geom_line(size = 0.8) +
geom_errorbar(aes(ymin = monthlyEvenness - monthlyEvennessSE, ymax = monthlyEvenness + monthlyEvennessSE), width=0.2) +
xlab("Months") +
ylab("J'") +
# ggtitle("Comparing the monthly species evenness (J') of phytoplankton counts between three consecutive years") +
theme_classic() +
theme(plot.title = element_text(hjust = 0.5))
#'*(1) ---------------*
# Shannon
shannon.YM <- dailyDesc %>%
group_by(months, month, year, Season) %>%
summarise(monthlyshannon = mean(shannon, na.rm = TRUE),
monthlyshannonN = n(),
monthlyshannonsD = sd(shannon, na.rm = TRUE),
monthlyshannonSE = sd(shannon, na.rm = TRUE)/sqrt(n())) %>%
ggplot(., aes(x = reorder(months, month), y = (monthlyshannon), col = year, group = year)) +
geom_point(size = 1) +
#geom_line(size = 0.8) +
geom_errorbar(aes(ymin = monthlyshannon - monthlyshannonSE, ymax = monthlyshannon + monthlyshannonSE), width=0.2) +
xlab("Months") +
ylab("H'") +
# ggtitle("Comparing the monthly Shannon (H') Index of Diversity of phytoplankton between three consecutive years") +
theme_classic() +
theme(plot.title = element_text(hjust = 0.5))
#'*(1) ----------------*
# Simpson
simpson.YM <- dailyDesc %>%
group_by(months, month, year, Season) %>%
summarise(monthlysimpson = mean(simpson, na.rm = TRUE),
monthlysimpsonN = n(),
monthlysimpsonsD = sd(simpson, na.rm = TRUE),
monthlysimpsonSE = sd(simpson, na.rm = TRUE)/sqrt(n())) %>%
ggplot(., aes(x = reorder(months, month), y = (monthlysimpson), col = year, group = year)) +
geom_point(size = 1) +
#geom_line(size = 0.8) +
geom_errorbar(aes(ymin = monthlysimpson - monthlysimpsonSE, ymax = monthlysimpson + monthlysimpsonSE), width=0.2) +
xlab("Months") +
ylab("Simpson Index of Diversity") +
ggtitle("Comparing the monthly Simpson Index of Diversity of phytoplankton between three consecutive years") +
theme_classic() +
theme(plot.title = element_text(hjust = 0.5))
# Figure out how to plot these
# Must 1 legend be shown? ANd remove middle ad top x axis labels?
# Capitalise legend title (labs(col = ""))
biomass.YM
richness.YM/evenness.YM/shannon.YM
simpson.YM
#'*This was added late*
# Plot dominants with others greyed out
# HAB species vs everything else relative abundance
# Get relative abundance per month for each species
month.rel <- as.data.frame(
make_relative(
as.matrix(
Sp_only %>%
ungroup() %>%
filter(Species != "Diatom") %>%
mutate(Sp_abb = abbreviate(Species, 5, strict = FALSE)) %>%
group_by(YEarMonth, Sp_abb) %>%
summarise(averageDens = mean(meanCells, na.rm = TRUE)) %>%
pivot_wider(names_from = Sp_abb, values_from = averageDens,
values_fill = list(averageDens = 0)) %>%
remove_rownames %>%
column_to_rownames(var="YEarMonth")))) %>%
rownames_to_column(var = "YEarMonth")
# add month order into df
monthOrder <- Sp_only %>%
ungroup() %>%
select(YEarMonth, months, month, year)
monthOrder$YEarMonth <- as.factor(monthOrder$YEarMonth)
Sp_abbre <- Sp_only %>%
ungroup() %>%
filter(Species != "Diatom") %>%
select(Species) %>%
mutate(Sp_abb = abbreviate(Species, 5, strict = FALSE)) %>%
distinct()
# join month order, reorder columns and pivot longer
month.rel <- month.rel %>%
ungroup() %>%
left_join(monthOrder, by = "YEarMonth", keep = FALSE) %>%
select(YEarMonth, months, month, year, everything()) %>%
pivot_longer(-c(YEarMonth, months, month, year), names_to = "Sp_abb", values_to = "abun") %>%
inner_join(Sp_abbre, by = "Sp_abb")
# find dom species
dom_sp <- month.rel %>%
group_by(months) %>%
top_n(1, abun) %>%
ungroup()
# calc top species for each month
dsp <- month.rel %>%
group_by(month, Species) %>%
summarise(meanRel = mean(abun, na.rm = TRUE)) %>%
ungroup() %>%
group_by(month) %>%
top_n(1, meanRel)
# plot yearly fluctuations for each of the top species
ggplot() +
geom_point(data = month.rel %>%
filter(Species %in% dsp$Species),
aes(x = reorder(months, month),
y = abun, group = year, col = year), size = 1) +
geom_line(data = month.rel %>%
filter(Species %in% dsp$Species),
aes(x = reorder(months, month),
y = abun, group = year, col = year), size = 1) +
facet_wrap(~Species, ncol = 2) +
theme_classic() +
xlab("Months") +
ylab("Relative abundance") +
labs(col = "Phytoplankton species") +
ggtitle("Fluctuations in the relative abundances of dominant phytoplankton species on a monthly aggregate") +
theme_classic() +
theme(plot.title = element_text(hjust = 0.5))
# phyto class diversity --------------------------------------------------
#'*[Stacked bar graph] of different phyto classes, by year-months -------------*
# from Sp_Only df all years together
Sp_only %>%
group_by(months, month, Season, Classification) %>%
summarise(monthlyMean = mean(meanCells, na.rm = TRUE)) %>%
ggplot(., aes(x = reorder(months, month), y = monthlyMean, fill = Classification)) +
geom_bar(stat = "Identity", position = "fill", col = "black") +
xlab("Months") +
ylab("Proportion monthly mean") +
labs(fill = "Phytoplankton class") +
ggtitle("Comparing the proportion of mean monthly counts between
phytoplankton classes over three consecutive years") +
theme_classic() +
theme(plot.title = element_text(hjust = 0.5))
#'*[Stacked bar graph] of different phyto classes, by months, facetd by year ------------------*
# from Sp_Only df facet_wrap(~year)
Sp_only %>%
group_by(months, month, year, Season, Classification) %>%
summarise(monthlyMean = mean(meanCells, na.rm = TRUE)) %>%
ggplot(., aes(x = reorder(months, month), y = monthlyMean, fill = Classification)) +
geom_bar(stat = "Identity", position = "fill", col = "Black") +
facet_wrap(~year, ncol = 1) +
xlab("Months") +
ylab("Proportion monthly mean") +
labs(fill = "Phytoplankton class") +
ggtitle("Comparing the proportion of mean monthly counts between phytoplankton
classes over three consecutive years") +
theme_classic() +
theme(plot.title = element_text(hjust = 0.5))
# NMDS --------------------------------------------------------------------
#'*Use to show all species 97 sp --*
# All species
allsp <- ggplot(data = mtet %>%
pivot_longer(-months, names_to = "Sp_abb", values_to = "abun") %>%
group_by(months) %>%
arrange(desc(abun)), aes(x = reorder(Sp_abb, abun), y = abun)) +
geom_col(position = "Dodge") +
coord_flip() +
xlab("Species") +
ylab("Relative abundance") +
ggtitle("Relative abundance of species (97)") +
theme_classic() +
theme(plot.title = element_text(hjust = 0.5))
#'*Use to show dom species species 73 sp --*
# Species above relative abundance of 0.01 per month
domsp <- ggplot(data = mtet %>%
pivot_longer(-months, names_to = "Sp_abb", values_to = "abun") %>%
group_by(months) %>%
arrange(desc(abun)) %>%
filter(abun > 0.01), aes(x = reorder(Sp_abb, abun), y = abun)) +
geom_col(position = "Dodge") +
coord_flip() +
xlab("Species") +
ylab("Relative abundance") +
ggtitle("Species with a relative abundance > 0.01 per month (73)") +
theme_classic() +
theme(plot.title = element_text(hjust = 0.5))
allsp / domsp
#'*[NMDS] check what it is based on i.e relative abundance or actual counts ------*
plot(NMDStestmonth)
ordiplot(NMDStestmonth,type="n")
orditorp(NMDStestmonth,display="species",col="red",air=0.01)
orditorp(NMDStestmonth,display="sites",cex=1.25,air=0.01)
## RED TIDE CLASSES --------------------------------------------------------
#'*Compare Red tide classes per month ---------*
# Compare the proportion of differnt red tide classes per month over the three years
ggplot(RT.classNumbers_monthYear, aes(x = reorder(months.x, month), y = n, fill = FWC_mod)) +
geom_bar(stat = "Identity", position = "fill", col = "black") +
facet_wrap(~year, ncol = 1) +
xlab("Months") +
ylab("Proportion number of days") +
labs(fill = "Red Tide class") +
ggtitle("The proportion of different red tide classes per month over the three years") +
theme_classic() +
theme(plot.title = element_text(hjust = 0.5))
## RESOURCES ---------------------------------------------------------------
#'*Compare overall cells vs dates, sized by counting times ------------*
# how do counting times change over time, linked to total cells
left_join(max_cells_day, Time_count, by = "Date") %>%
filter(count_times > 0) %>%
ggplot(., aes(x = Date, y = log(totalCells))) +
geom_point(aes(col = FWC_mod, size = count_times)) +
geom_line() +
xlab("Date") +
ylab("Log Phytoplankton counts") +
labs(col = "Red Tide class", size = "Samples collected per day") +
ggtitle("Investigating how sampling frequency varied in relation to
red tide severity in 2019") +
theme_classic() +
theme(plot.title = element_text(hjust = 0.5))
#'* USE TO COMPARE EXCLUDING ALL vs BELOW AVERAGE REGRESSIONS [Scatter and relationship line (smooth)] used to show counting times correlation, above daily counting average ------------*
# correlation between total cells counted and numebr of times counted per day
ggplot(left_join(max_cells_day, Time_count, by = "Date") %>%
filter(count_times > 0), aes(x = log(totalCells), y = count_times)) +
geom_point(aes(col = FWC_mod)) +
geom_hline(yintercept = 3.17, size = 1) +
# geom_smooth(method = "lm", aes(col = FWC_mod), se = FALSE) +
# geom_smooth(method = "loess", se = FALSE) +
xlab("Log Cells/L") +
ylab("Number samples collected per day") +
labs(col = "Red Tide class") +
ggtitle("Relationship between Cells/L counted and number of samples collected per day,
when different Red tide severity classes were experienced") +
theme_classic() +
theme(plot.title = element_text(hjust = 0.5))
Chapter 3
####### Chapter 3 Filters ################
## PHYTOPLANKTON FLUCTUATIONS BETWEEN SITES --------------------------------
# summarise down to monthly measurements to compare sites [boxplot graph]
#'* Graphs for Aim 1a -------------*
# DO NOT USE
# biomass
ggplot(data = dailydesc_filter, aes(x = reorder(months, month), y = total_biomass, fill = Site)) +
geom_boxplot() +
xlab("Months") +
ylab("Cells/L") +
labs(fill = "Site") +
ggtitle("Compare the difference in biomass of phytoplankton between the two sites, for three consecutive months") +
theme_classic() +
theme(plot.title = element_text(hjust = 0.5))
# Line version
ggplot(data = dailydesc_filter, aes(x = Date, y = total_biomass, col = Site)) +
geom_line(size = 1) +
facet_wrap(~reorder(months, month), ncol = 1, scales = "free_x")
# Point with errorbar version
Sp.only_filter %>%
group_by(months, month, year, YEarMonth, Site) %>%
summarise(mean_cells = mean(meanCells, na.rm = TRUE),
n_cells = n(),
sd_cells = sd(meanCells, na.rm = TRUE),
se_cells = sd(meanCells, na.rm = TRUE)/sqrt(n())) %>%
ggplot(., aes(x = reorder(months, month), y = mean_cells, col = Site, group = Site)) +
geom_point() +
# geom_line() +
geom_errorbar(aes(ymin = mean_cells - se_cells, ymax = mean_cells + se_cells), width=0.2) +
xlab("Months") +
ylab("Cells/L") +
labs(fill = "Site") +
# ggtitle("") +
theme_classic() +
theme(plot.title = element_text(hjust = 0.5))
## Point errorbars can be done for the metrics too if necessary ##
# richness
ggplot(data = dailydesc_filter, aes(x = reorder(months, month), y = S, fill = Site)) +
geom_boxplot() +
xlab("Months") +
ylab("Number of species") +
labs(fill = "Site") +
ggtitle("Compare the difference in species richness of phytoplankton between
the two sites, for three consecutive months of 2019") +
theme_classic() +
theme(plot.title = element_text(hjust = 0.5))
# evenness
ggplot(data = dailydesc_filter, aes(x = reorder(months, month), y = J, fill = Site)) +
geom_boxplot() +
xlab("Months") +
ylab("Pielou's (J') Evenness") +
labs(fill = "Site") +
ggtitle("Compare the difference in species evenness (J') of phytoplankton between
the two sites, for three consecutive months of 2019") +
theme_classic() +
theme(plot.title = element_text(hjust = 0.5))
# diversity
ggplot(data = dailydesc_filter, aes(x = reorder(months, month), y = shannon, fill = Site)) +
geom_boxplot() +
xlab("Months") +
ylab("Shannon Index of Diversity") +
labs(fill = "Site") +
ggtitle("Compare the difference in Shannon index of diversity between phytoplankton species between
the two sites, for three consecutive months of 2019") +
theme_classic() +
theme(plot.title = element_text(hjust = 0.5))
## DOMINANT SPECIES DENSITIES BETWEEN SITES --------------------------------
#'* used to back up ind spe graph (Aim 2), can possibly do stats on this ---------*
# [Stacked bar graph] comparing PS and ADf biomass on monthly level (more broad than above)
Sp.only_filter %>%
group_by(Site, months, month) %>%
summarise(monthly.mean = mean(meanCells, na.rm = TRUE)) %>%
ggplot(., aes(x = reorder(months, month), y = monthly.mean, fill = Site)) +
geom_bar(stat = "Identity", position = "fill") +
geom_hline(yintercept = 0.5, col = "black") +
xlab("Months") +
ylab("Proportion momthly mean Cells/L") +
labs(fill = "Site") +
ggtitle("The proportion of cells counted between the Primary sump and After Drumfilter
for three consecutive months of 2019") +
theme_classic() +
theme(plot.title = element_text(hjust = 0.5))
#'* Graph for Aim 2 -----------*
# [Stacked bargraph] of Top n species, comparing monthly averages between two sites
Sp.only_filter %>%
group_by(Site, months, month, Species, Classification) %>%
summarise(monthly.mean = mean(meanCells, na.rm = TRUE)) %>%
filter(Species %in% top_species$Species) %>%
ggplot(., aes(x = reorder(months, month), y = monthly.mean, fill = Site)) +
geom_bar(stat = "Identity", position = "fill") +
geom_hline(yintercept = 0.5, col = "black") +
#coord_flip() +
facet_wrap(~ reorder(Species, -monthly.mean), ncol = 2) +
xlab("Months") +
ylab("Proportion momthly mean Cells/L") +
labs(fill = "Site") +
ggtitle("The proportion of cells counted between the Primary sump and After Drumfilter for the
top four dominant species for three consecutive months of 2019") +
theme_classic() +
theme(plot.title = element_text(hjust = 0.5))
